WO2020248848A1 - Procédé et dispositif de détermination intelligente de cellule anormale, et support d'informations lisible par ordinateur - Google Patents

Procédé et dispositif de détermination intelligente de cellule anormale, et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2020248848A1
WO2020248848A1 PCT/CN2020/093546 CN2020093546W WO2020248848A1 WO 2020248848 A1 WO2020248848 A1 WO 2020248848A1 CN 2020093546 W CN2020093546 W CN 2020093546W WO 2020248848 A1 WO2020248848 A1 WO 2020248848A1
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cell
gray
cell set
value
abnormal
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PCT/CN2020/093546
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English (en)
Chinese (zh)
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郭冰雪
王季勇
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion

Definitions

  • This application relates to the field of artificial intelligence technology, and in particular to an intelligent abnormal cell judgment method, device, and computer-readable storage medium.
  • Abnormal cells such as cancer cells
  • cancer cells are often the fuse for humans to produce major illnesses. According to surveys, there are 500,000 new cases and 274,000 deaths worldwide each year, of which 85% of the new cases are due to early identification of abnormal cells The reason for the low recognition rate.
  • cervical cancer is currently the only cancer that can be detected and cured early, so early recognition is crucial for the treatment of the disease.
  • the inventor found that the cell fluid inspection method is currently the most commonly used method for identifying abnormal cells.
  • This application provides an intelligent method and device for judging abnormal cells, and a computer-readable storage medium. Its main purpose is to simplify, quickly and accurately determine whether the cell picture or video contains abnormalities when the user inputs a cell picture or video. Cell and output the judgment result.
  • an intelligent method for judging abnormal cells includes:
  • the obtaining a cell set includes:
  • the mucosa and secretions of the cells after the staining process are photographed to obtain the cell set.
  • the noise reduction adopts the following adaptive image noise reduction filtering method:
  • (x, y) represents the coordinates of the image pixels in the cell set
  • f(x, y) is the output data of the cell set after noise reduction processing based on the adaptive image noise reduction filtering method
  • ⁇ (x,y) is noise
  • g(x,y) is the set of cells
  • Is the total variance of the noise of the cell set Is the average gray value of the pixel (x, y)
  • L represents the current pixel coordinates.
  • the image segmentation of the cell set completed by the preprocessing operation based on the multi-threshold segmentation model includes:
  • the inter-class variance set is calculated, and the inter-class variance with the largest value in the inter-class variance set is selected to reset the gray interval of the cell set to obtain more Gray scale cell set.
  • the inter-class variance set ⁇ T is:
  • T is the preset threshold interval
  • t 1 is not less than the value
  • t m is not greater than the value 255
  • the present application also provides an intelligent abnormal cell judgment device, which includes a memory and a processor, and the memory stores an intelligent abnormal cell judgment program that can run on the processor.
  • the intelligent abnormal cell judgment program is executed by the processor, the following steps are implemented:
  • the obtaining a cell set includes:
  • the mucosa and secretions of the cells after the staining process are photographed to obtain the cell set.
  • the noise reduction adopts the following adaptive image noise reduction filtering method:
  • (x, y) represents the coordinates of the image pixels in the cell set
  • f(x, y) is the output data of the cell set after noise reduction processing based on the adaptive image noise reduction filtering method
  • ⁇ (x,y) is noise
  • g(x,y) is the set of cells
  • Is the total variance of the noise of the cell set Is the average gray value of the pixel (x, y)
  • L represents the current pixel coordinates.
  • the gray-level division of the cell set completed by the preprocessing operation based on a multi-threshold segmentation model to obtain a multi-level gray-scale cell set includes:
  • the inter-class variance set is calculated, and the inter-class variance with the largest value in the inter-class variance set is selected to reset the gray interval of the cell set to obtain more Gray scale cell set.
  • the inter-class variance set ⁇ T is:
  • T is the preset threshold interval
  • t 1 is not less than the value
  • t m is not greater than the value 255
  • this application also provides a computer-readable storage medium that stores an intelligent abnormal cell judgment program, and the intelligent abnormal cell judgment program can be used by one or more The processor executes to implement the steps of the intelligent abnormal cell judgment method as described above.
  • the noise reduction process can reduce the impact of noise on cell images, and the nine-channel cell set can be calculated through the Hessian matrix, which can further improve the The feature extraction is to maximize the use of existing cell features.
  • the abnormal cell judgment model described in this application has excellent feature analysis capabilities, and can efficiently and accurately analyze whether the picture contains abnormal cells. Therefore, this application can achieve accurate Intelligent abnormal cell judgment function.
  • FIG. 1 is a schematic flowchart of an intelligent abnormal cell judgment method provided by an embodiment of the application
  • FIG. 2 is a schematic diagram of the internal structure of an intelligent abnormal cell judgment device provided by an embodiment of the application.
  • FIG. 3 is a schematic diagram of modules of an intelligent abnormal cell judgment program in an intelligent abnormal cell judgment device provided by an embodiment of the application.
  • This application provides an intelligent method for judging abnormal cells.
  • FIG. 1 it is a schematic flowchart of an intelligent abnormal cell judgment method provided by an embodiment of this application.
  • the method can be executed by a device, and the device can be implemented by software and/or hardware.
  • the intelligent method for judging abnormal cells includes:
  • the operation of obtaining the cell set includes: obtaining the mucosa and secretions of the cells, staining the mucosa and secretions, and photographing the mucosa and secretions of the cells after the staining process to obtain the cell set, And respectively mark whether each picture in the cell set contains abnormal cells to obtain the label set.
  • this application is based on the scraper rotating around the cell site to be detected to obtain the mucosa and secretions of the cells to be detected, and smear the mucosa and secretions on the plexiglass and perform a staining process, and the display
  • the micro device photographs the mucous membrane and secretion cells in the organic glass, and finally obtains the cell set.
  • the dyeing treatment first fixes the plexiglass coated with mucous membranes and secretions in an alcohol liquid, and then places a hematoxylin staining agent into the alcohol liquid, and finally achieves the reduction of the mucous membranes and secretions.
  • the purpose of staining the cells is to ensure that the cells are staind.
  • the noise reduction adopts the following adaptive image noise reduction filtering method:
  • (x, y) represents the coordinates of the image pixels in the cell set
  • f(x, y) is the output data of the cell set after noise reduction processing based on the adaptive image noise reduction filtering method
  • ⁇ (x,y) is noise
  • g(x,y) is the set of cells
  • Is the total variance of the noise of the cell set Is the average gray value of the pixel (x, y)
  • L represents the current pixel coordinates.
  • the contrast enhancement is to increase the difference between the maximum value and the minimum value of the brightness in the cell set picture, because cells with low contrast will affect the subsequent judgment of abnormal cells.
  • the contrast enhancement adopts the following method:
  • a is the linear slope and b is the intercept on the Y axis. If a>1, the output image contrast is enhanced compared to the original image. If a ⁇ 1, the output image contrast is The contrast of the original image is reduced, where D a represents the gray value of the cell set, and D b represents the gray value of the output cell set.
  • the preferred embodiment of the present application traverses the gray values of the image pixels in the cell set completed by the preprocessing operation, counts the number of times each gray value appears, and calculates each gray based on the total number of pixels in the cell set.
  • the occurrence probability of the degree value is calculated based on the preset threshold interval and the occurrence probability of each gray value to obtain the inter-class variance set, and the inter-class variance set with the largest value in the inter-class variance set is selected to reset the cell set Gray-scale interval, obtain multi-level gray-scale cell set.
  • the calculation method is:
  • t i t i+1 belongs to t 1 , t 2 ,...t m , and t 1 , t 2 ,...t m respectively represent preset thresholds, and t 1 , t 2 until t m are in increasing form, t 1 Not less than the value 0, t m is not greater than the value 255, further:
  • n i is the number of occurrences of each gray value, i is in the range of 0 to 255, and N is the total number of occurrences of gray values.
  • the between-class variance set ⁇ T is
  • T is the preset threshold interval
  • t 1 is not less than the value
  • t m is not greater than the value 255
  • the resetting method for resetting the gray-scale interval of the cell set is:
  • t 1 ⁇ Tmax is the maximum variance between clusters
  • I(x,y) is the gray value of the cell set
  • (x,y) is the coordinate of each pixel in the cell set.
  • the method calculates the new gray value, and calculates other pixels in turn, until the final multi-level gray cell set is obtained.
  • the Hessian matrix is a matrix constructed by high-order differentiation of an image and capable of reflecting image characteristics.
  • This application preferably first calculates the scale space function I ab of the multi-level gray-scale cell set, obtains the Hessian matrix based on the inverse derivation of the scale space function I ab , and uses the Hessian matrix to solve the multi Two feature value maps corresponding to each channel in the RGB gray-scale cell set, and the feature maps are added to the original channel of the multi-level gray-scale cell set to obtain a nine-channel cell set.
  • the scale space function I ab is:
  • ab has the same meaning as the parameter in the contrast enhancement
  • a is the linear slope
  • b is the intercept on the Y axis
  • ⁇ is the scale space function parameter
  • I(x,y) is the gray scale of each channel of the cell set.
  • this application includes three channels of R, G, and B, and G(X, Y; ⁇ ) is a Gaussian function.
  • the Gaussian function is:
  • Hessian matrix H is obtained by the reverse derivation process of the scale space function I ab :
  • x n and y n represent the coordinates of different pixels in the multi-level gray-scale cell set, and the eigenvalue ⁇ of the corresponding determinant is solved based on the Hessian matrix H:
  • the eigenvalue ⁇ is generally set in three dimensions, namely ⁇ 1 , ⁇ 2 , and ⁇ 3 .
  • the Hessian matrix H is solved for the three color channels to obtain H R , H G , H B , and then the The eigenvalues of the three color channels obtain ⁇ R1 , ⁇ R2 , ⁇ R3 , ⁇ G1 , ⁇ G2 , ⁇ G3 , ⁇ B1 , ⁇ B2 , and ⁇ B3 to form a nine-channel matrix. Therefore, the nine-channel matrix is solved for each pixel of the multi-level gray-scale cell set based on the above method, and a nine-channel cell set ⁇ RGB is finally obtained.
  • the abnormal cell judgment model includes a feature extraction layer and an abnormal cell recognition layer, and is based on a convolutional neural network.
  • the feature extraction layer receives the nine-channel cell set, and performs feature extraction based on a convolution operation and a pooling operation, and the convolution operation is:
  • ⁇ ' is the output value after the convolution operation, generally in the form of a multi-dimensional matrix
  • ⁇ RGB is the nine-channel cell set
  • k is the size of the convolution kernel, usually 2*2 dimensions
  • s is the stride of the convolution operation, which can take the value 1
  • p is the data zero-filling matrix.
  • the output value after the convolution operation is subjected to the pooling operation, and the pooling operation searches for the value with the largest matrix value among the output values of the convolution operation and forms a feature set ⁇ .
  • the present application inputs the feature set and the label set to the abnormal cell identification layer, and the abnormal cell identification layer performs the convolution operation on the feature set and then inputs it to the activation function to obtain a judgment value set , Inputting the judgment value set and the label set into a loss value calculated based on a loss function, and if the loss value is less than a preset threshold, the abnormal cell judgment model exits training.
  • the activation function is:
  • n is the size of the label set
  • y t is the judgment value set
  • ⁇ t is the label set.
  • S5. Receive the user's cell picture and input it into the abnormal cell judgment model to judge whether the cell includes abnormal cells and output the judgment result.
  • the abnormal cell judgment model recognizes that the cell set A contains abnormal cells and outputs the judgment result.
  • the output mode includes screen printing or voice broadcast And so on.
  • the invention also provides an intelligent device for judging abnormal cells.
  • FIG. 2 it is a schematic diagram of the internal structure of an intelligent abnormal cell judgment device provided by an embodiment of this application. (Corresponding modification)
  • the intelligent abnormal cell judging device 1 may be a PC (Personal Computer, personal computer), or a terminal device such as a smart phone, a tablet computer, or a portable computer, or a server.
  • the intelligent abnormal cell judgment device 1 at least includes a memory 11, a processor 12, a communication bus 13, and a network interface 14.
  • the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc.
  • the memory 11 may be an internal storage unit of the intelligent abnormal cell judgment device 1 in some embodiments, for example, the hard disk of the intelligent abnormal cell judgment device 1.
  • the memory 11 may also be an external storage device of the intelligent abnormal cell judging device 1, such as a plug-in hard disk or a smart media card (SMC) equipped on the intelligent abnormal cell judging device 1. Secure Digital (SD) card, flash card (Flash Card), etc.
  • SD Secure Digital
  • flash card Flash Card
  • the memory 11 may also include both an internal storage unit of the intelligent abnormal cell judgment device 1 and an external storage device.
  • the memory 11 can be used not only to store application software and various data installed in the intelligent abnormal cell judgment device 1, such as the code of the intelligent abnormal cell judgment program 01, etc., but also to temporarily store data that has been output or will be output. .
  • the processor 12 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip, and is used to run the program code or processing stored in the memory 11 Data, such as execution of intelligent abnormal cell judgment program 01, etc.
  • CPU central processing unit
  • controller microcontroller
  • microprocessor or other data processing chip
  • the communication bus 13 is used to realize the connection and communication between these components.
  • the network interface 14 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface), and is usually used to establish a communication connection between the device 1 and other electronic devices.
  • the device 1 may also include a user interface.
  • the user interface may include a display (Display) and an input unit such as a keyboard (Keyboard).
  • the optional user interface may also include a standard wired interface and a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light emitting diode) touch device, etc.
  • the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the intelligent abnormal cell judgment device 1 and to display a visualized user interface.
  • Figure 2 only shows the intelligent abnormal cell judging device 1 with components 11-14 and the intelligent abnormal cell judging program 01. Those skilled in the art will understand that the structure shown in Figure 1 does not constitute an intelligent abnormal
  • the definition of the cell judgment device 1 may include fewer or more components than shown, or a combination of certain components, or different component arrangements.
  • the memory 11 stores the intelligent abnormal cell judgment program 01; when the processor 12 executes the intelligent abnormal cell judgment program 01 stored in the memory 11, the following steps are implemented:
  • Step 1 Obtain a cell set and a label set, and perform preprocessing operations including noise reduction and contrast enhancement on the cell set.
  • the operation of obtaining the cell set includes: obtaining the mucosa and secretions of the cells, staining the mucosa and secretions, and photographing the mucosa and secretions of the cells after the staining process to obtain the cell set, And respectively mark whether each picture in the cell set contains abnormal cells to obtain the label set.
  • this application is based on the scraper rotating one week at the cell site to be detected to obtain the mucosa and secretions of the cells to be detected, and smear the mucosa and secretions on the organic glass and perform staining treatment, and at the same time, the display
  • the micro device photographs the mucous membrane and secretion cells in the organic glass, and finally obtains the cell set.
  • the dyeing treatment first fixes the plexiglass coated with mucous membranes and secretions in an alcohol liquid, and then places a hematoxylin staining agent into the alcohol liquid, and finally achieves the reduction of the mucous membranes and secretions.
  • the purpose of staining the cells is to ensure that the cells are staind.
  • the noise reduction adopts the following adaptive image noise reduction filtering method:
  • (x, y) represents the coordinates of the image pixels in the cell set
  • f(x, y) is the output data of the cell set after noise reduction processing based on the adaptive image noise reduction filtering method
  • ⁇ (x,y) is noise
  • g(x,y) is the set of cells
  • Is the total variance of the noise of the cell set Is the average gray value of the pixel (x, y)
  • L represents the current pixel coordinates.
  • the contrast enhancement is to increase the difference between the maximum value and the minimum value of the brightness in the cell set picture, because cells with low contrast will affect the subsequent judgment of abnormal cells.
  • the contrast enhancement adopts the following method:
  • a is the linear slope and b is the intercept on the Y axis. If a>1, the output image contrast is enhanced compared to the original image. If a ⁇ 1, the output image contrast is The contrast of the original image is reduced, where D a represents the gray value of the cell set, and D b represents the gray value of the output cell set.
  • Step 2 Perform gray-scale division on the cell set completed by the preprocessing operation based on a multi-threshold segmentation model to obtain a multi-level gray-scale cell set.
  • the preferred embodiment of the present application traverses the gray values of the image pixels in the cell set completed by the preprocessing operation, counts the number of times each gray value appears, and calculates each gray based on the total number of pixels in the cell set.
  • the occurrence probability of the degree value is calculated based on the preset threshold interval and the occurrence probability of each gray value to obtain the inter-class variance set, and the inter-class variance set with the largest value in the inter-class variance set is selected to reset the cell set Gray-scale interval, obtain multi-level gray-scale cell set.
  • the calculation method is:
  • t i t i+1 belongs to t 1 , t 2 ,...t m , and t 1 , t 2 ,...t m respectively represent preset thresholds, and t 1 , t 2 until t m are in increasing form, t 1 Not less than the value 0, t m is not greater than the value 255, further:
  • b i is the number of occurrences of each gray value, i is in the range of 0-255, and N is the total number of occurrences of gray value.
  • the between-class variance set ⁇ T is
  • T is the preset threshold interval
  • t 1 is not less than the value
  • t m is not greater than the value 255
  • the resetting method for resetting the gray-scale interval of the cell set is:
  • t 1 ⁇ Tmax is the maximum variance between clusters
  • I(x,y) is the gray value of the cell set
  • (x,y) is the coordinate of each pixel in the cell set.
  • the method calculates the new gray value, and calculates other pixels in turn, until the final multi-level gray cell set is obtained.
  • Step 3 Calculate the Hessian matrix based on the multi-level gray-scale cell set and obtain a nine-channel cell set.
  • the Hessian matrix is a matrix constructed by high-order differentiation of an image and capable of reflecting image characteristics.
  • This application preferably first calculates the scale space function I ab of the multi-level gray-scale cell set, obtains the Hessian matrix based on the inverse derivation of the scale space function I ab , and uses the Hessian matrix to solve the multi Two feature value maps corresponding to each channel in the RGB gray-scale cell set, and the feature maps are added to the original channel of the multi-level gray-scale cell set to obtain a nine-channel cell set.
  • the scale space function I ab is:
  • ab has the same meaning as the parameter in the contrast enhancement
  • a is the linear slope
  • b is the intercept on the Y axis
  • ⁇ is the scale space function parameter
  • I(x,y) is the gray scale of each channel of the cell set.
  • this application includes three channels of R, G, and B, and G(X, Y; ⁇ ) is a Gaussian function.
  • the Gaussian function is:
  • Hessian matrix H is obtained by the reverse derivation process of the scale space function I ab :
  • x n and y n represent the coordinates of different pixels in the multi-level gray-scale cell set, and the eigenvalue ⁇ of the corresponding determinant is solved based on the Hessian matrix H:
  • the eigenvalue ⁇ is generally set in three dimensions, namely ⁇ 1 , ⁇ 2 , and ⁇ 3 .
  • the Hessian matrix H is solved for the three color channels to obtain H R , H G , H B , and then the The eigenvalues of the three color channels obtain ⁇ R1 , ⁇ R2 , ⁇ R3 , ⁇ G1 , ⁇ G2 , ⁇ G3 , ⁇ B1 , ⁇ B2 , and ⁇ B3 to form a nine-channel matrix. Therefore, the nine-channel matrix is solved for each pixel of the multi-level gray-scale cell set based on the above method, and a nine-channel cell set ⁇ RGB is finally obtained.
  • Step 4 Input the nine-channel cell set and label set into the abnormal cell judgment model for training, until the abnormal cell judgment model meets the preset training exit condition and then exits the training.
  • the abnormal cell judgment model includes a feature extraction layer and an abnormal cell recognition layer, and is based on a convolutional neural network.
  • the feature extraction layer receives the nine-channel cell set, and performs feature extraction based on a convolution operation and a pooling operation, and the convolution operation is:
  • ⁇ ' is the output value after the convolution operation, generally in the form of a multi-dimensional matrix
  • ⁇ RGB is the nine-channel cell set
  • k is the size of the convolution kernel, usually 2*2 dimensions
  • s is the stride of the convolution operation, which can take the value 1
  • p is the data zero-filling matrix.
  • the output value after the convolution operation is subjected to the pooling operation, and the pooling operation searches for the value with the largest matrix value among the output values of the convolution operation and forms a feature set ⁇ .
  • the present application inputs the feature set and the label set to the abnormal cell identification layer, and the abnormal cell identification layer performs the convolution operation on the feature set and then inputs it to the activation function to obtain a judgment value set , Inputting the judgment value set and the label set into a loss value calculated based on a loss function, and if the loss value is less than a preset threshold, the abnormal cell judgment model exits training.
  • the activation function is:
  • n is the size of the label set
  • y t is the judgment value set
  • ⁇ t is the label set.
  • Step 5 Receive the user's cell picture and input it into the abnormal cell judgment model to judge whether the cell includes abnormal cells and output the judgment result.
  • the abnormal cell judgment model recognizes that the cell set A contains abnormal cells and outputs the judgment result.
  • the output mode includes screen printing or voice broadcast And so on.
  • the intelligent abnormal cell judgment program can also be divided into one or more modules, and the one or more modules are stored in the memory 11 and run by one or more processors (this embodiment For example, it is executed by the processor 12) to complete this application.
  • the module referred to in this application refers to a series of computer program instruction segments that can complete specific functions, used to describe the intelligent abnormal cell judgment program in the intelligent abnormal cell judgment device The implementation process.
  • the intelligent abnormal cell determination program can be divided into The data receiving module 10, the data processing module 20, the model training module 30, and the intelligent abnormal cell judgment output module 40 are exemplary:
  • the data receiving module 10 is configured to obtain a cell set and a label set, and perform preprocessing operations including noise reduction and contrast enhancement on the cell set.
  • the data processing module 20 is configured to: perform gray-scale division on the cell set completed by the preprocessing operation based on a multi-threshold segmentation model to obtain a multi-level gray cell set, and calculate the Hessian matrix based on the multi-level gray cell set And get the nine-channel cell set.
  • the model training module 30 is configured to input the nine-channel cell set and the label set into an abnormal cell judgment model for training, until the abnormal cell judgment model meets a preset training exit condition and then exit training.
  • the intelligent abnormal cell judgment output module 40 is configured to receive a user's cell picture and input it into the abnormal cell judgment model to determine whether the cell includes an abnormal cell and output the judgment result.
  • the above-mentioned data receiving module 10, data processing module 20, model training module 30, intelligent abnormal cell judgment output module 40 and other program modules implement functions or operation steps that are substantially the same as those in the above-mentioned embodiment, and will not be repeated here.
  • the embodiment of the present application also proposes a computer-readable storage medium.
  • the computer-readable storage medium may be non-volatile or volatile.
  • the computer-readable storage medium stores an intelligent abnormal cell judgment program, and the intelligent abnormal cell judgment program can be executed by one or more processors to achieve the following operations:

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Abstract

L'invention concerne un procédé de détermination intelligente de cellule anormale, un dispositif de détermination intelligente de cellule anormale et un support d'informations lisible par ordinateur. Ledit procédé consiste à : acquérir un ensemble de cellules et un ensemble d'étiquettes, et appliquer une opération de prétraitement à l'ensemble de cellules (S1) ; appliquer, sur la base d'un modèle de segmentation multi-seuil, une division de niveaux de gris à l'ensemble de cellules auquel a été appliquée l'opération de prétraitement pour obtenir un ensemble de cellules à niveaux de gris multi-niveau (S2) ; calculer une matrice hessienne sur la base de l'ensemble de cellules à niveaux de gris multi-niveau, et obtenir un ensemble de cellules à neuf canaux (S3) ; entrer l'ensemble de cellules à neuf canaux et l'ensemble d'étiquettes dans un modèle de détermination de cellule anormale à des fins d'apprentissage, et jusqu'à ce que le modèle de détermination de cellule anormale satisfasse une condition de sortie d'apprentissage prédéfinie, quitter l'apprentissage (S4) ; recevoir une image de cellule d'un utilisateur, et entrer cette dernière dans le modèle de détermination de cellule anormale pour déterminer si la cellule comprend une cellule anormale et délivrer le résultat de détermination (S5), de façon à exécuter la détermination intelligente de cellule anormale. Le procédé met en œuvre la fonction de détermination intelligente et précise de cellule anormale.
PCT/CN2020/093546 2019-06-14 2020-05-29 Procédé et dispositif de détermination intelligente de cellule anormale, et support d'informations lisible par ordinateur WO2020248848A1 (fr)

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CN113096077A (zh) * 2021-03-25 2021-07-09 深圳力维智联技术有限公司 异常比例检测方法、装置、设备及计算机可读存储介质
CN117876380A (zh) * 2024-03-13 2024-04-12 昆明昊拜农业科技有限公司 一种烟叶环境温湿度、微层差预测方法及系统

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110363747A (zh) * 2019-06-14 2019-10-22 平安科技（深圳）有限公司 智能化异常细胞判断方法、装置及计算机可读存储介质
CN110415212A (zh) * 2019-06-18 2019-11-05 平安科技（深圳）有限公司 异常细胞检测方法、装置及计算机可读存储介质
CN111652845A (zh) * 2020-04-27 2020-09-11 平安科技（深圳）有限公司 异常细胞自动标注方法、装置、电子设备及存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015142923A1 (fr) * 2014-03-17 2015-09-24 Carnegie Mellon University Procédés et systèmes de classification de maladies
CN108364032A (zh) * 2018-03-27 2018-08-03 哈尔滨理工大学 一种基于卷积神经网络的宫颈癌细胞图片识别算法
CN108376400A (zh) * 2018-02-12 2018-08-07 华南理工大学 一种骨髓细胞自动分类方法
CN109308695A (zh) * 2018-09-13 2019-02-05 镇江纳兰随思信息科技有限公司 基于改进U-net卷积神经网络模型的癌细胞识别方法
CN110363747A (zh) * 2019-06-14 2019-10-22 平安科技（深圳）有限公司 智能化异常细胞判断方法、装置及计算机可读存储介质

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108197606A (zh) * 2018-01-31 2018-06-22 浙江大学 一种基于多尺度膨胀卷积的病理切片中异常细胞的识别方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015142923A1 (fr) * 2014-03-17 2015-09-24 Carnegie Mellon University Procédés et systèmes de classification de maladies
CN108376400A (zh) * 2018-02-12 2018-08-07 华南理工大学 一种骨髓细胞自动分类方法
CN108364032A (zh) * 2018-03-27 2018-08-03 哈尔滨理工大学 一种基于卷积神经网络的宫颈癌细胞图片识别算法
CN109308695A (zh) * 2018-09-13 2019-02-05 镇江纳兰随思信息科技有限公司 基于改进U-net卷积神经网络模型的癌细胞识别方法
CN110363747A (zh) * 2019-06-14 2019-10-22 平安科技（深圳）有限公司 智能化异常细胞判断方法、装置及计算机可读存储介质

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JIANG, XIANGANG ET AL.: "Extraction method of brain vessels based on multi-threshold Otsu and Hessian matrix", COMPUTER ENGINEERING AND DESIGN, vol. 35, no. 5, 31 May 2014 (2014-05-31), ISSN: 1000-7024, DOI: 20200813084945Y *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113096077A (zh) * 2021-03-25 2021-07-09 深圳力维智联技术有限公司 异常比例检测方法、装置、设备及计算机可读存储介质
CN113096077B (zh) * 2021-03-25 2024-05-03 深圳力维智联技术有限公司 异常比例检测方法、装置、设备及计算机可读存储介质
CN117876380A (zh) * 2024-03-13 2024-04-12 昆明昊拜农业科技有限公司 一种烟叶环境温湿度、微层差预测方法及系统
CN117876380B (zh) * 2024-03-13 2024-05-14 昆明昊拜农业科技有限公司 一种烟叶环境温湿度、微层差预测方法及系统

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